Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data

3D vessel reconstruction based on intra-operative intravascular ultrasound for robotic autonomous catheter navigation

In recent years, robotic technology has improved instrument navigation precision and accuracy, and helped decrease the complexity of minimally invasive surgery. Still, the inherent restricted access to the anatomy of the patients severely complicates many procedures. Interventionists frequently depend on external technologies for visual guidance, usually employing ionizing radiation, due to the limited view upon the surgical scene. In the case of endovascular procedures, fluoroscopy is the common imaging modality used for visualization. This modality is based on X-rays and only offers a two- dimensional (2D) view of the surgical scene. Having a real-time, up-to-date understanding of the surrounding environment of the surgical instruments within the vasculature and not depending on using ionizing radiation would not only be very helpful for interventionists, but also paramount for the navigation of an intraluminal robot. Therefore, the aim of this thesis is to develop an algorithm able to do an intra-operative and real-time three-dimensional (3D) vessel reconstruction. The algorithm is divided into two parts: the reconstruction and the merging. In the first one, it is obtained the 3D vessel reconstruction of a section of the vessel and in the second one, the different sections of 3D vessel reconstruction are combined. A real vessel mesh is used to calculate the fitting errors of the reconstructed vessel which are very smallEn los últimos años, la tecnología robótica ha mejorado la precisión y fiabilidad de la navegación de instrumentos y ha ayudado a disminuir la complejidad de la cirugía mínimamente invasiva. Aún así, el acceso restringido inherente a la anatomía de los pacientes complica gravemente muchos procedimientos. Los intervencionistas dependen con frecuencia de tecnologías externas para la guía visual, generalmente empleando radiación ionizante, debido a la visión limitada de la escena quirúrgica. En el caso de los procedimientos endovasculares, la fluoroscopia es la modalidad de imagen común utilizada para la visualización. Esta modalidad se basa en rayos X y solo ofrece una vista bidimensional (2D) de la escena quirúrgica. Poder saber en tiempo real y de forma actualizada como es el entorno alrededor de los instrumentos quirúrgicos que se encuentran dentro de la vasculatura y no depender del uso de radiación ionizante no solo sería muy útil para los intervencionistas, sino también fundamental para la navegación de un robot intraluminal. Por lo tanto, el objetivo de esta tesis es desarrollar un algoritmo capaz de realizar una reconstrucción tridimensional (3D) del vaso sanguíneo de forma intraoperatoria y en tiempo real. El algoritmo se divide en dos partes: la reconstrucción y la unión. En la primera se obtiene la reconstrucción 3D de una sección del vaso sanguíneo y en el segundo se combinan las diferentes secciones obtenidas de vasos sanguíneos reconstruidos en 3D. Se utiliza una malla de un vaso sanguíneo real para calcular los errores de ajuste del vaso sanguíneo reconstruido, son errores muy pequeñosEn els últims anys, la tecnologia robòtica ha millorat la precisió i la fiabilitat de la navegació dels instruments i ha ajudat a disminuir la complexitat de la cirurgia mínimament invasiva. Tot i així, l'accés restringit inherent a l'anatomia dels pacients complica greument molts procediments. Els intervencionistes sovint depenen de tecnologies externes per a la guia visual, normalment emprant radiacions ionitzants, a causa de la visió limitada de l'escena quirúrgica. En el cas dels procediments endovasculars, la fluoroscòpia és la modalitat d'imatge comuna utilitzada per a la visualització. Aquesta modalitat es basa en raigs X i només ofereix una visió bidimensional (2D) de l'escena quirúrgica. Poder saber en temps real i de forma actualitzada com és l'entorn al voltant dels instruments quirúrgics que es troben dins de la vasculatura i no depèn de l'ús de radiació ionitzant no només seria molt útil per als intervencionistes, sinó també fonamental per a la navegació d'un robot intraluminal. Per tant, l'objectiu d'aquesta tesi és desenvolupar un algorisme capaç de fer una reconstrucció tridimensional (3D) del vas sanguini de forma intraoperatòria i en temps real. L'algorisme es divideix en dues parts: la reconstrucció i la fusió. En la primera s'obté la reconstrucció en 3D d'una secció del vas sanguini i en la segona, es combinen les diferents seccions obtingudes de vasos sanguinis reconstruïts en 3D. S'utilitza una malla d’un vas sanguini real per calcular els errors d'ajust del vas sanguini reconstruït, els errors son molt petit

State estimation of a cheetah spine and tail using an inertial sensor network

The cheetah (Acinonyx jubatus) is by far the most manoeuvrable and agile terrestrial animal. Little is known, in terms of biomechanics, about how it achieves these incredible feats of manoeuvrability. The transient motions of the cheetah all involve rapid flicking of its tail and flexing of its spine. The aim of the research was to develop tools (hardware and software) that can be used to gain a better understanding of the cheetah tail and spine by capturing its motion. A mechanical rig was used to simulate the tail and spine motion. This insight may inspire and aid in the design of bio-inspired robotic platforms. A previous assumption was that the tail is heavy and acts as a counter balance or rudder, yet this was never tested. Contrary to this assumption, necropsy results determined that the tail was in fact light with a relatively low inertia value. Fur from the tail was used in wind tunnel experiments to determine the drag coefficient of a cheetah tail. No researchers have actively sought to track the motion of a cheetah's spine and tail during rapid manoeuvres via placing multiple sensors on a cheetah. This requires the development of a 3D dynamic model of the spine and tail to accurately study the motion of the cheetah. A wireless sensor network was built and three different filters and state estimation algorithms were designed and validated with a mechanical rig and camera system. The sensor network consists of three sensors on the tail (base, middle and tip) as well as a hypothetical collar sensor (GPS and WiFi were not implemented)

A hybrid hair model using three dimensional fuzzy textures

Cataloged from PDF version of article.Human hair modeling and rendering have always been a challenging topic in computer graphics. The techniques for human hair modeling consist of explicit geometric models as well as volume density models. Recently, hybrid cluster models have also been successful in this subject. In this study, we present a novel three dimensional texture model called 3D Fuzzy Textures and algorithms to generate them. Then, we use the developed model along with a cluster model to give human hair complex hairstyles such as curly and wavy styles. Our model requires little user effort to model curly and wavy hair styles. With this study, we aim at eliminating the drawbacks of the volume density model and the cluster hair model with 3D fuzzy textures. A three dimensional cylindrical texture mapping function is introduced for mapping purposes. Current generation graphics hardware is utilized in the design of rendering system enabling high performance rendering.Aran, Medeni ErolM.S

Registration And Feature Extraction From Terrestrial Laser Scanner Point Clouds For Aerospace Manufacturing

Aircraft wing manufacture is becoming increasingly digitalised. For example, it is becoming possible to produce on-line digital representations of individual structural elements, components and tools as they are deployed during assembly processes. When it comes to monitoring a manufacturing environment, imaging systems can be used to track objects as they move about the workspace, comparing actual positions, alignments, and spatial relationships with the digital representation of the manufacturing process. Active imaging systems such as laser scanners and laser trackers can capture measurements within the manufacturing environment, which can be used to deduce information about both the overall stage of manufacture and progress of individual tasks. This paper is concerned with the in-line extraction of spatial information such as the location and orientation of drilling templates which are used with hand drilling tools to ensure drilled holes are accurately located. In this work, a construction grade terrestrial laser scanner, the Leica RTC360, is used to capture an example aircraft wing section in mid-assembly from several scan locations. Point cloud registration uses 1.5"white matte spherical targets that are interchangeable with the SMR targets used by the Leica AT960 MR laser tracker, ensuring that scans are connected to an established metrology control network used to define the coordinate space. Point cloud registration was achieved to sub-millimetre accuracy when compared to the laser tracker network. The location of drilling templates on the surface of the wing skin are automatically extracted from the captured and registered point clouds. When compared to laser tracker referenced hole centres, laser scanner drilling template holes agree to within 0.2mm

The Larmor frequency shift of a white matter magnetic microstructure model with multiple sources

Magnetic susceptibility imaging may provide valuable information about chemical composition and microstructural organization of tissue. However, its estimation from the MRI signal phase is particularly difficult as it is sensitive to magnetic tissue properties ranging from the molecular to macroscopic scale. The MRI Larmor frequency shift measured in white matter (WM) tissue depends on the myelinated axons and other magnetizable sources such as iron-filled ferritin. We have previously derived the Larmor frequency shift arising from a dense media of cylinders with scalar susceptibility and arbitrary orientation dispersion. Here we extend our model to include microscopic WM susceptibility anisotropy as well as spherical inclusions with scalar susceptibility to represent subcellular structures, biologically stored iron etc. We validate our analytical results with computer simulations and investigate the feasibility of estimating susceptibility using simple iterative linear least squares without regularization or preconditioning. This is done in a digital brain phantom synthesized from diffusion MRI (dMRI) measurements of an ex vivo mouse brain at ultra-high field.Comment: 70 pages, 14 figure